Students’ Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology

“However, a marked improvement in AI’s capabilities, particularly in the realm of generative AI, became apparent in last decade (Adiwardana et al., 2020; Vaswani et al., 2017).”

“One of the most prominent theories used to explain and predict technology acceptance is the “Unified Theory of Acceptance and Use of Technology” (UTAUT) (Venkatesh et al., 2003).”

“If they are more distinct, a limit of 0.85 should be used (Henseler et al., 2015).”

“According to Hair et al. (2011), R 2 values can generally be interpreted as weak (0.25), moderate (0.50), or substantial (0.75).”

“The original study by Venkatesh et al. (2012) did not provided how the actual use was measured.”

“Even as a considerable volume of studies on AI in education emerged in the past years, signifying a booming interest, the actual effects of applications like ChatGPT on higher education institutions and stakeholders remain enigmatic (Zawacki-Richter et al., 2019).”

“The reliability and validity criteria were met for each construct, and discriminant validity was confirmed (Hair et al., 2013).”

“Historical traces of AI’s foray into education can be linked to the development of early chat-bots in the 1960s (Weizenbaum, 1966).”

“Personal innovativeness (PI) has been acknowledged as important factor that impact technology adoption and usage (Agarwal & Prasad, 1998).”

“Cotton et al. (2023) and Tlili et al. (2023) discuss maintaining academic integrity, while Williamson et al. (2023) encourage more research into AI’s educational impact.”

“Venkatesh et al. (2016) recommended using UTAUT2 as a fundamental model to establish hypotheses regarding the connections between proposed variables and user adoption of technology.”

“Venkatesh et al. (2012) and Limayem et al. (2007) have explained HT as a perceptual construct, and it has been found to be a significant predictor of “Behavioral intention” and technology use (Tamilmani et al., 2019).”

“Additionally, Dwivedi et al. (2019) noted that previous research in this field typically utilized only a portion of the UTAUT2 model and frequently neglected moderators.”

“Cotton et al. (2023) and Tlili et al. (2023) discuss maintaining academic integrity, while Williamson et al. (2023) encourage more research into AI’s educational impact.”

“Van Dis et al. (2023) recognize its benefits in democratizing knowledge but call for debates on authorship and accountability.”

“The potential impact of AI on academia and the need for ethical use policies is discussed by Lund et al. (2023), while Thorp (2023) cautions against naming AI as a co-author.”

“Naumowa (2023) stresses critical thinking in using AI, whereas Hsu (2023), Ivanov and Soliman (2023), Lim et al. (2023), Crawford et al. (2023), Sullivan et al. (2023), and Cooper (2023), explore the ethical and practical implications, calling for ethical guidelines and theoretical frameworks.”

“Naumowa (2023) stresses critical thinking in using AI, whereas Hsu (2023), Ivanov and Soliman (2023), Lim et al. (2023), Crawford et al. (2023), Sullivan et al. (2023), and Cooper (2023), explore the ethical and practical implications, calling for ethical guidelines and theoretical frameworks.”

“Interviews explore its potential in improving content creation and academic research, with a focus on ethical implications (Lund & Wang, 2023; Pavlik, 2023).”

“The connection between Personal Innovativeness (PI) and acceptance and usage of technology has been thoroughly investigated in the literature (Slade et al., 2015).”

“Interviews explore its potential in improving content creation and academic research, with a focus on ethical implications (Lund & Wang, 2023; Pavlik, 2023).”

“Naumowa (2023) stresses critical thinking in using AI, whereas Hsu (2023), Ivanov and Soliman (2023), Lim et al. (2023), Crawford et al. (2023), Sullivan et al. (2023), and Cooper (2023), explore the ethical and practical implications, calling for ethical guidelines and theoretical frameworks.”

“Con-versations with ChatGPT reveal its capabilities and limitations, particularly in areas like plagiarism and misuse in education (Eysenbach, 2023).”

“UTAUT2 is a prominent theoretical model that aims to understand the factors that influence individuals’ adoption and use of new technologies in organizational and personal contexts (Tamilmani et al., 2021).”

“The chatbot generates coherent and informative text, and can be accessed in many countries (Liebrenz et al., 2023).”

“The integration of Artificial Intelligence (AI) into various sectors of society has become a prominent focal point of academic inquiry and application, with education standing out as a particularly noteworthy arena (Nazaretsky et al., 2022a).”

“Naumowa (2023) stresses critical thinking in using AI, whereas Hsu (2023), Ivanov and Soliman (2023), Lim et al. (2023), Crawford et al. (2023), Sullivan et al. (2023), and Cooper (2023), explore the ethical and practical implications, calling for ethical guidelines and theoretical frameworks.”

“…“Social influence” to have a significant effect on “Behavioral intention” in areas such as mobile learning (Kang et al., 2015) and e-learning systems (El-Masri & Tarhini, 2017), while others did not find such significance, such as in the study of Alotumi (2022) or Kumar & Bervell (2019) on…”

“One can point to the surge of interest in AI’s educational potential over the last decade, juxtaposed with a landscape marked by exaggerated claims and often inconclusive findings (Bates et al., 2020).”

“ChatGPT in university education presents challenges to the academic community wherever ChatGPT is available (Teubner et al., 2023).”

“However, our findings contradict those of studies that 1 3 Innovative Higher Education (2024) 49:223–245 examined the use of learning management systems (Ain et al., 2016) and blackboard learning systems (Raza et al., 2022).”

“…that have demonstrated a notable and affirmative correlation between “Performance expectancy” and “Behavioral intention” in areas such as mobile learning (Arain et al., 2019), e-learning platforms (Azizi et al., 2020; Nikolopoulou et al., 2020), and learning management software (Raman & Don, 2013).”

“…earlier studies on the use of emerging technologies in higher education, including Google Classroom (Kumar & Bervell, 2019), animation (Dajani & Abu Hegleh, 2019), mobile technologies (Hu et al., 2020), blended learning (Azizi et al., 2020), and augmented reality technology (Faqih & Jaradat, 2021).”

“Naumowa (2023) stresses critical thinking in using AI, whereas Hsu (2023), Ivanov and Soliman (2023), Lim et al. (2023), Crawford et al. (2023), Sullivan et al. (2023), and Cooper (2023), explore the ethical and practical implications, calling for ethical guidelines and theoretical frameworks.”

“This finding is in line with earlier studies on the use of emerging technologies in higher education, including Google Classroom (Kumar & Bervell, 2019), animation (Dajani & Abu Hegleh, 2019), mobile technologies (Hu et al., 2020), blended learning (Azizi et al., 2020), and augmented reality technology (Faqih & Jarada…”

“The explained variances were higher than in a study conducted by Maican et al. (2019) on communication and collaboration applications in academic environments, as well as in a study conducted by Hoi (2020) on how the mobile devices are accepted and used for language learning by students.”

“…that have demonstrated a notable and affirmative correlation between “Performance expectancy” and “Behavioral intention” in areas such as mobile learning (Arain et al., 2019), e-learning platforms (Azizi et al., 2020; Nikolopoulou et al., 2020), and learning management software (Raman & Don, 2013).”

“…that have demonstrated a notable and affirmative correlation between “Performance expectancy” and “Behavioral intention” in areas such as mobile learning (Arain et al., 2019), e-learning platforms (Azizi et al., 2020; Nikolopoulou et al., 2020), and learning management software (Raman & Don, 2013).”

“…earlier studies on the use of emerging technologies in higher education, including Google Classroom (Kumar & Bervell, 2019), animation (Dajani & Abu Hegleh, 2019), mobile technologies (Hu et al., 2020), blended learning (Azizi et al., 2020), and augmented reality technology (Faqih & Jaradat, 2021).”

“…such as e-learning systems (Raza et al., 2022), mobile applications (Ameri et al., 2020; Kang et al., 2015), immersive virtual and augmented reality (Bower et al., 2020; Faqih & Jaradat, 2021), and learning management software (Alotumi, 2022; Jakkaew & Hemrungrote, 2017; Kumar & Bervell, 2019).”

“…theory, has been shown to be a significant predictor of “Behavioral intention” in studies such as Raffaghelli et al.’s (2022) exploration of students’ acceptance of an early warning system in higher education and Mehta et al.’s (2019) investigation of the influence of values on e-learning adoption.”

“…that have demonstrated a notable and affirmative correlation between “Performance expectancy” and “Behavioral intention” in areas such as mobile learning (Arain et al., 2019), e-learning platforms (Azizi et al., 2020; Nikolopoulou et al., 2020), and learning management software (Raman & Don, 2013).”

“Our research affirms the strong association between “Performance expectancy” and “Habit”, which is consistent with previous studies by Venkatesh et al. (2012) and Lewis et al. (2013) on how emerging technologies are accepted in higher education.”

“Cotton et al. (2023) and Tlili et al. (2023) discuss maintaining academic integrity, while Williamson et al. (2023) encourage more research into AI’s educational impact.”

“Recent studies also examine how these factors can be affected by different contextual factors, such as the COVID-19 pandemic (Osei et al., 2022; Zacharis & Nikolopoulou, 2022), and how they can be utilized to improve the design and implementation of educational technology tools.”

“The integration of Artificial Intelligence (AI) into various sectors of society has become a prominent focal point of academic inquiry and application, with education standing out as a particularly noteworthy arena (Nazaretsky et al., 2022a).”

“The explained variances were higher than in a study conducted by Maican et al. (2019) on communication and collaboration applications in academic environments, as well as in a study conducted by Hoi (2020) on how the mobile devices are accepted and used for language learning by students.”

“Nevertheless, our results are not the same as those of Twum et al. (2022), who found no direct effect of “Habit” on “Behavioral intention” to use e-learning, and Raman & Don (2013) and Ain et al. (2016), who found no direct impact of “Habit” on “Behavioral intention” to use learning management…”

“A number of scholars “have suggested that personality traits, such as personal innovativeness, play a significant role in technology adoption in the domain of Information Technology (IT)” (Farooq et al., 2017; Sitar-Taut & Mican, 2021).”

“…using Google Classroom (Alotumi, 2022; Kumar & Bervell, 2019), mobile learning (Ameri et al., 2020; Arain et al., 2019; Kang et al., 2015; Nikolopou-lou et al., 2020) e-learning platform (El-Masri & Tarhini, 2017; Twum et al., 2022) and learning management system (Ain et al., 2016; Zwain, 2019).”

“UTAUT “synthesizes the concepts and user experiences that provide the foundation for theories on the user acceptance process of an information system” (Yu et al., 2021).”

“Recent studies also examine how these factors can be affected by different contextual factors, such as the COVID-19 pandemic (Osei et al., 2022; Zacharis & Nikolopoulou, 2022), and how they can be utilized to improve the design and implementation of educational technology tools.”

“Previous studies on the adoption of animation (Dajani & Abu Hegleh, 2019) and Google Classroom in higher education (Alotumi, 2022; Kumar & Bervell, 2019) have usually found that “Facilitating conditions” have no effect.”

“…it has been shown to positively influence acceptance and use of lecture caption systems (Farooq et al., 2017), mobile learning during social distancing (Sitar-Taut & Mican, 2021), and Google Classroom platform for mobile learning (Alotumi, 2022; Jakkaew & Hemrungrote, 2017; Kumar & Bervell, 2019).”

“Venkatesh et al. (2012) and Limayem et al. (2007) have explained HT as a perceptual construct, and it has been found to be a significant predictor of “Behavioral intention” and technology use (Tamilmani et al., 2019).”

“…affecting students’ or teachers’ intentions to use different technology tools such as e-learning systems (Raza et al., 2022), mobile applications (Ameri et al., 2020; Kang et al., 2015), immersive virtual and augmented reality (Bower et al., 2020; Faqih & Jaradat, 2021), and learning management…”

“This finding is in line with earlier studies on the use of emerging technologies in higher education, including Google Classroom (Kumar & Bervell, 2019), animation (Dajani & Abu Hegleh, 2019), mobile technologies (Hu et al., 2020), blended learning (Azizi et al., 2020), and augmented reality technology (Faqih & Jarada…”

“Previous studies have found “Social influence” to have a significant effect on “Behavioral intention” in areas such as mobile learning (Kang et al., 2015) and e-learning systems (El-Masri & Tarhini, 2017), while others did not find such significance, such as in the study of Alotumi (2022) or Kumar…”

“…UTAUT2 model is used to identify factors affecting students’ or teachers’ intentions to use different technology tools such as e-learning systems (Raza et al., 2022), mobile applications (Ameri et al., 2020; Kang et al., 2015), immersive virtual and augmented reality (Bower et al., 2020; Faqih &…”

“Moreover, accompanying this technological fervor are grave concerns about intellectual property, data privacy, potential biases, and equitable access to resources (Li et al., 2022).”

“Naumowa (2023) stresses critical thinking in using AI, whereas Hsu (2023), Ivanov and Soliman (2023), Lim et al. (2023), Crawford et al. (2023), Sullivan et al. (2023), and Cooper (2023), explore the ethical and practical implications, calling for ethical guidelines and theoretical frameworks.”

“Consequently, there has been an outright discouragement of their usage by some academic journals and HEIs (Chan, 2023; Strzelecki, 2023; Vincent, 2023).”

“Second, the direct effect of CGPTAW on CGPTAI has not been discussed in prior studies (Maheshwari, 2023; Strzelecki, 2023); thus, this study examined the awareness of ChatGPT as a novel technology and its effect on students’ ultimate intention to adopt the technology.”

“Past studies (Shahzad et al., 2024 ; Strzelecki, 2024 ) focused on different theo - ries, such as UTAUT and TAM models.”

“Similarly, Strzelecki [28] researched students’ acceptance of ChatGPT, finding that the constructs of habit and performance expectancy in UTAUT had the most impact on the behavioral intention for use.”

“However, in other studies (e.g., Habibi et al., 2023; Strzelecki, 2023b), SI was found to be only a weak predictor of BI.”

“This technology stands out for its ability to understand and generate language-based responses, making it a valuable tool in the educational sector [7].”

“Moreover, it can serve as a dynamic assessor and an adaptive indicator of student performance on assignments (Ivanov & Soliman, 2023 ; Strzelecki, 2024).”

“Each construct in the study satisfied the reliability and validity standards, and the discriminant validity was verified, with the scale tested recently in a study of ChatGPT use among students (Strzelecki, 2024).”

“The survey instrument used in this study was developed based on existing studies (e.g., Amani et al., 2023; Strzelecki, 2023, 2024; Strzelecki et al., 2024; White et al., 2024) that investigated individuals’ perceptions of the use of generative AI tools in learning environments.”

“This consistent finding underscores the need for educational technologies to clearly demonstrate their practical benefits to encourage widespread use among students and educators [5,27–30].”

“Other studies have considered best practices for working in collaboration with GenAI, suggesting opportunities at various levels of study (Kasneci et al. 2023), as well as the acceptance of students in adopting this new technology (Strzelecki 2024).”

“However, the weaknesses are that cultural nuances have not been explored much, especially in studies like [36,39], which could have benefited from a deeper analysis of how sociocultural factors influence adoption.”

“Considering CG-AI applications such as ChatGPT are relatively new tools, the factors that influence new users’ perception of such technologies are not yet properly understood.”

“Previous studies indicate that behav - ioral intention is the most significant factor in explaining students’ usage behavior of ChatGPT in higher education (Strzelecki, 2024; Romero-Rodríguez et al., 2024).”

“Similarly, Strzelecki ’ s research (Strzelecki, 2023) indicates that individual differences, including gender, moderate the acceptance of ChatGPT according to the Unified Theory of Acceptance and Use of Technology (UTAUT).”

“The rapid evolution of technology, such as ChatGPT, and changes in the educational environment (Wardat et al., 2023a) are compelling teachers globally at all levels to reassess their teaching and learning approaches, especially regarding the use of AI (Strzelecki, 2023).”

“…the widespread influence of effort expectancy on intention to use ChatGPT in recent studies (Budhathoki et al., 2024; Duong et al., 2024; Grassini et al., 2024; Strzelecki, 2024), ease of use does not always impact intention to use (Bervell et al., 2021;; Habibi et al., 2023; Sudan et al., 2024).”

“Regarding effort expectancy, earlier studies showed it was positively associated with attitudes toward AI (Gansser & Reich, 2021; Strzelecki, 2023), but its association with behavioral intentions was inconsistent in different studies using AI for general purposes.”

“Perceived value is one of the many variables that may influence a student's acceptance and use of GenAI in higher education (Chan & Zhou, 2023; Strzelecki, 2024).”

“Following Strzelecki (2024), this study operationalized the use of ChatGPT by adopting a seven-point scale, ranging from “never” to “several times a day” to capture the varying levels of user engagement with the AI-service.”

“Additionally, bootstrapping was implemented to ascertain the statistical significance of the PLS-SEM estimates (Ringle et al., 2022; Strzelecki, 2024).”

“This finding diverges somewhat from other studies, where students have displayed more favorable perceptions towards using this tool [72,75,89,96,105,109].”

“This practice of expanding the UTAUT model by incorporating new variables has been acknowledged in the literature, indicating its adoption by other researchers and potential to produce valuable insights 32 .”

“Concepts related to performance expectancy, such as outcome expectations, job fit, and the like, can predict learners’ intention to use ChatGPT (Kelly et al., 2023; Menon & Shilpa, 2023; Strzelecki, 2023).”

“Wijaya et al. (2024), Grassini et al. (2024), and Yee et al. (2024) reported a nonsignificant impact of social influence, whereas Zheng et al. (2024), Strzelecki (2023), and Du and Lv (2024) found this factor to be significant.”

“As other research studies (Firaina & Sulisworo, 2023; Montenegro-Rueda et al., 2023; Strzelecki, 2023) indicate, ChatGPT can thus contribute to promoting autonomous learning, curiosity, and innovative and critical thinking in students.”

“…the extensive literature on AI tools in education, which predominantly examines the perspectives of academic instructors, scientists, and researchers, there exists a notable gap in research regarding students' perceptions of incorporating AI tools like ChatGPT into higher education settings [10].”

“This finding is indicative of ChatGPT’s growing popularity as a tool in higher education as indicated by several recent studies (Abdaljaleel et al., 2024; Faruk et al., 2023; Ibrahim et al., 2023; Strzelecki, 2023; Tiwari et al., 2023; von Garrel & Mayer, 2023; Weidener & Fischer, 2024).”

“These results are consistent with those of Liu and Ma (2024), Qu and Wu (2024), Strzelecki (2024), and Zou and Huang (2023).”

“For ChatGPT to seamlessly integrate across multiple domains, it is important to consider these complicated aspects of pricing value (Strzelecki, 2023).”

“Similarly, Strzelecki et al. (2024) found that HM predicts the learners ’ behavioral intention to utilize ChatGPT for educational reasons.”

“Evidence in New Zealand (Du & Alm, 2024), Sweden (Ou et al., 2024), and Poland (Belda-Medina & Calvo-Ferrer, 2022; Strzelecki, 2024) also confirms the extent of research on GenAI adoption and student attitudes within higher education.”

“Strzelecki (2023) suggests that wise use of AI tools, such as ChatGPT, would ease the job of providing immediate feedback to students’ learning activities, creating tailored content, and designing assessments.”

“In the context of AI in education, this implies that if AI tools are perceived as useful in improving teaching methods or learning processes, their adoption and continued use are more likely (Strzelecki, 2023; Strzelecki & ElArabawy, 2024).”

“Unlike users in other regions, mainland Chinese users lack access to international AI tools such as ChatGPT or Claude and instead rely on China-specific AI tools like DeepSeek and Doubao.”

“ChatGPT, created by OpenAI, was officially introduced on November 30, 2022, and has subsequently garnered significant interest (Strzelecki, 2024).”

“UTAUT2 framework is particularly appropriate for academic settings, as it has been empirically validated across contexts and is adaptable to the nuanced dynamics of higher education technology use (Al Farsi, 2023; Strzelecki, 2024).”

“The extended UTAUT (UTAUT2) model was recently validated in explaining higher education students ’ ChatGPT utilization, with substantial explanatory power (Strzelecki 2024).”

“Nevertheless, in other studies, FC was not found to significantly impact IU [13],[23].”

“Various studies have implemented it within different AI industries, indicating a high correlation between the different sub-scales (Ma & Huo, 2023; Strzelecki, 2023a, 2023b).”

“We are challenged to use new tools in all educational disciplines [1–4], adapt to rapidly shifting technological land-scapes, share what we have learned to support one another and engage in intellectual curiosity [5, 6].”

“The chatbot ’ s ability to generate human-like text and provide rapid, contextually relevant responses positions it as a groundbreaking innovation in academia, particularly in facilitating personalized learning, brainstorming, content creation, and academic support (Strzelecki, 2024; Walter, 2024).”

“Facilitating Conditions mostly do not seem to impact the intention to use Chat - GPT (Grassini et al., 2024; Strzelecki, 2023, 2024; Strzelecki & ElArabawy, 2024).”